Horizontal Federating Decision Tree Learning From Data Streams: Building Intelligence in IoT Edge Networks

被引：0

作者：

Sharma, Shachi ^{[1
]}

Arora, Kanishka ^{[1
]}

Thakur, Prem S. P. ^{[1
]}

机构：

[1] South Asian Univ, Dept Comp Sci, New Delhi, India

来源：

2022 IEEE 8TH WORLD FORUM ON INTERNET OF THINGS, WF-IOT | 2022年

关键词：

Data stream; edge network; federated learning; IoT gateway; performance analysis;

D O I：

10.1109/WF-IOT54382.2022.10152105

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper presents Horizontal Federated Decision Tree Learning (HFDTL) system which is capable of building a collaborative model from evolving data streams in federated environment such as that of edge network where IoT gateways play the role of edge nodes processing data locally and edge server aggregates the local models using newly proposed majority-based aggregation algorithm. The popular VFDT algorithm is modified for edge nodes. The performance analysis of the HFDTL system reveals that it results in more accuracy compared to centralized VFDT for balanced partitioned data streams whereas the accuracy remains lower for unbalanced partitioned data. The communication cost also remains slightly higher for unbalanced data streams. The deployment of HFDTL system is expected to bring intelligence in IoT edge networks.

引用

页数：6

共 49 条

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